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Online Communication as a Potential Travel Medicine Research Tool: Analysis of Messages Posted on the TravelMed Listserv

2009· article· en· W2042745510 on OpenAlexaff
Liane Macdonald, Douglas W. MacPherson, Brian D. Gushulak

Bibliographic record

VenueJournal of Travel Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsThe InternetMedicineDemographicsInformation seeking behaviorSocial mediaFamily medicineMedical educationWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Access to the Internet and electronic mail has created opportunities for online discussion that can facilitate medical education and clinical problem solving. Research into the use of these information technologies is increasing and the analysis of these tools can support and guide the activities of professional organizations, including educational endeavors. OBJECTIVE: The initial objective was to analyze patterns of information exchange on the International Society of Travel Medicine's (ISTM) travel health electronic mailing list related to a specific area of society interest. Secondary objectives included the analysis of listserv use in relation to subscriber demographics and rates of participation to support travel health educational activities. METHODS: This study examined the use of the ISTM TravelMed listserv over an 8-month period from January 1, 2006, to July 31, 2006. Descriptive data analysis included TravelMed user demographics, the type of posting, the topic and frequency of postings, and the source of information provided. RESULTS: During the study period, 911 (47%) of the eligible ISTM members subscribed to the TravelMed listserv. About 369 of these subscribers posted 1,710 individual messages. About 1,506 (88%) postings were educational; 207 (12%) postings were administrative. A total of 389 (26%) of the educational postings were primary queries and 1,120 (74%) were responses, with a mean string length of 2.9 responses per query (range: 1-51). Twenty participants contributed 40% of the educational postings. The topics with the most frequent postings were vaccines and vaccine-preventable diseases (473/31%) and malaria (258/17%). Postings focused on special populations, including pregnant women or immigrants, comprised a total of 14 postings (<1%). CONCLUSIONS: During the study period, a limited number of ISTM members (19%) authored postings on the listserv. Regular discussion centered on a limited number of recurring topics. The analysis provides several opportunities for the support of educational initiatives, clinical problem solving, and program evaluation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.121
GPT teacher head0.446
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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